AWS or Google Cloud for a Life Sciences Consulting Practice

Neither platform makes you HIPAA compliant automatically. Both AWS and Google Cloud will sign a business associate agreement covering the specific services you use, and both publish a list of which services fall under that agreement. Before starting a project with protected health information involved, confirm with the client which services are actually in scope and get the business associate agreement in place before any data moves, not after.

Before any protected health information moves, confirm these points:

• Get a business associate agreement in place with the cloud provider before protected health information enters the environment.
• Confirm with the client which specific services are in scope for that agreement, using each provider's published list.
• Remember that neither platform makes you HIPAA compliant automatically, so your own configuration and controls still matter.
• Document who is responsible for each control, so the client knows what the platform covers and what you own.

If a client's process needs to satisfy 21 CFR Part 11 recordkeeping requirements, the infrastructure question becomes less about the cloud platform and more about your documented change control, audit trail and validation testing on top of it. This validation burden exists on either AWS or Google Cloud in roughly equal measure. Pick the platform your team can document and validate efficiently rather than assuming one platform carries less validation overhead than the other; neither one does the validation work for you.

Both platforms offer cold and archival storage tiers that make sense for datasets you access rarely but must retain for years, and both charge for data egress when you move that data out. The practical difference shows up in tooling: Google Cloud's Genomics and life sciences-specific services integrate tightly with BigQuery for large-scale variant analysis, while AWS has a broader ecosystem of life sciences partner tools and a longer track record in this specific space. If a client already has petabytes sitting on one platform, the cost of moving it is usually the deciding factor over any tooling preference.

Some life sciences clients, particularly ones running lab instruments or specialized equipment, need infrastructure that bridges an on-premises lab network with cloud storage and compute. Both platforms offer hybrid connectivity options built for exactly this. The deciding factor is usually which platform's hybrid tooling your consulting team has actually used successfully before, since this is not a category where you want to be learning the connectivity patterns for the first time on a live client engagement.

Be honest that some lock-in is close to unavoidable once you're using a platform's specialized genomics or analytics services, and frame the real question as whether the client's raw data stays in an open, portable format even if the analysis tooling around it doesn't. Store raw sequencing or imaging data in standard, well-documented formats rather than a platform-proprietary one, so a future migration is a data transfer problem rather than a data reformatting problem.

Some clinical research clients answer to an institutional review board or ethics committee that may impose its own data handling requirements beyond general HIPAA obligations, and these requirements can vary by institution in ways a general cloud compliance page won't capture. Ask the client directly what their IRB has already approved or required for data infrastructure before proposing an architecture, since a technically sound proposal that conflicts with an existing IRB approval means starting over.

Build a short intake checklist for new life sciences engagements that captures this up front: applicable regulations, existing IRB or ethics board requirements, and any prior infrastructure commitments the client has already made. Consultants who skip this step sometimes discover a conflicting requirement midway through a build, which is a far more expensive time to find it.

Where an IRB requirement and a cloud platform's default configuration genuinely conflict, resolve it in writing with the client before you build anything, even if that means a slower start. A documented resolution protects both you and the client if the same question comes up again during the study's own ethics review, and it gives your team a clear precedent to reuse on the next engagement with a similar requirement instead of starting the conversation from scratch every time, which is where a lot of consulting hours quietly get wasted re-litigating the same question with a different client's counsel.

Keep this precedent library organized by requirement type rather than by client, so a new engagement with a similar constraint can pull from it directly instead of a team member trying to recall which past project handled something comparable.